QUEST: Quantile-Conditioned Selection of Historical Context for Probabilistic Time Series Forecasting

Probabilistic time series forecasting (PTSF) plays an important role in decision-making across a wide range of real-world applications, including economics, transportation, energy, and AIOps. Recent direct prediction methods offer an efficient way to produce probabilistic forecasts without assuming a predefined distribution family or relying on iterative sampling. However, existing methods often construct a shared historical context for different parts of the predictive distribution, while the relevance of historical information may vary across quantile levels. This motivates historical context construction that explicitly accounts for the target probability level. In this paper, we propose QUEST, a probabilistic time series forecasting model based on quantile-conditioned historical selection. Specifically, QUEST divides the observed history into patches and encodes them into shared local representations. For each target quantile, a corresponding query interacts with these representations to determine patch weights conditioned on both the observed history and the target probability level, yielding a quantile-specific predictive context. A shared decoder then combines each context with its corresponding query to predict the target quantile over the entire forecast horizon, enabling multiple quantile forecasts in a single forward pass. Extensive experiments on eight real-world multivariate datasets demonstrate that QUEST achieves state-of-the-art probabilistic forecasting performance.